newLISP and any other interactive language system will constantly generate new memory
objects during expression evaluation. The new memory objects are intermediate evaluation results,
reassigned memory objects, or memory objects whose content was changed. If newLISP did not
delete some of the objects created, it would eventually run out of available memory.

In order to understand newLISP's automatic memory management, it is necessary to first
review the traditional methods employed by other languages.

Traditional automatic memory management (Garbage Collection)

In most programming languages, a process registers allocated memory, and another
process finds and recycles the unused parts of the allocated memory pool. The recycling
process can be triggered by some memory allocation limit or can be scheduled to happen
between evaluation steps. This form of automatic memory management is called
Garbage Collection.

Traditional garbage collection schemes developed for LISP employed one of two
algorithms: ¹

(1) The mark-and-sweep algorithm registers each allocated memory object. A mark
phase periodically flags each object in the allocated memory pool. A named object
(a variable symbol) directly or indirectly references each memory object in the system.
The sweep phase frees the memory of the marked objects when they are no longer in use.

(2) A reference-counting scheme registers each allocated memory object together
with a count of references to the object. This reference count gets incremented or decremented
during expression evaluation. Whenever an object's reference count reaches zero, the object's
allocated memory is freed.

Over time, many elaborate garbage collection schemes have been attempted based on these
principles. The first garbage collection algorithms appeared in LISP. The inventors of
the Smalltalk language used more elaborate garbage collection schemes. The history of
Smalltalk-80 is an exciting account of the challenges of implementing memory management
in an interactive programming language;
see [Glenn Krasner, 1983: Smalltalk-80, Bits of History, Words of Advice].
A more recent overview of garbage collection methods can be found in
[Richard Jones, Rafael Lins, 1996: Garbage Collection, Algorithms for
Automatic Dynamic Memory Management].

One reference only, (ORO) memory management

Memory management in newLISP does not rely on a garbage collection algorithm. Memory is
not marked or reference-counted. Instead, a decision whether to delete a newly created memory
object is made right after the memory object is created.

Empirical studies of LISP have shown that most LISP cells are not shared and so can be
reclaimed during the evaluation process. Aside from some optimizations for part of the
built-in functions, newLISP deletes memory new objects containing intermediate evaluation results
once it reaches a higher evaluation level. newLISP does this by pushing a reference to each
created memory object onto a result stack. When newLISP reaches a higher evaluation level, it
removes the last evaluation result's reference from the result stack and deletes the evaluation
result's memory object. This should not be confused with one-bit reference counting. ORO memory
management does not set bits to mark objects as sticky.

newLISP follows a one reference only (ORO) rule. Every memory object not referenced by a
symbol is obsolete once newLISP reaches a higher evaluation level during
expression evaluation. Objects in newLISP (excluding symbols and contexts) are passed by value
copy to other user-defined functions. As a result, each newLISP object only requires one
reference.

newLISP's ORO rule has advantages. It simplifies not only memory management but also
other aspects of the newLISP language. For example, while users of traditional LISP
have to distinguish between equality of copied memory objects and equality of references
to memory objects, newLISP users do not.

newLISP's ORO rule forces newLISP to constantly allocate and then free LISP cells.
newLISP optimizes this process by allocating large chunks of cell memory from the host
operating system. newLISP will request LISP cells from a free cell list and then recycle
those cells back into that list. As a result, only a few CPU instructions (pointer assignments)
are needed to unlink a free cell or to re-insert a deleted cell.

The overall effect of ORO memory management is a faster evaluation time and a smaller memory
and disk footprint than traditional interpreted LISP's can offer. Time spent linking and
unlinking memory objects is more than compensated for by the lack of processing time used in
traditional garbage collection. ORO memory management also avoids occasional processing
pauses seen in languages using traditional garbage collection and the tuning of garbage
collection parameters required when running memory intensive programs.

ORO memory management happens synchronous to other processing in the interpreter, which
results in deterministic processing times.

In versions before 10.1.3, newLISP employed a classic mark and sweep algorithm to
free un-referenced cells under error conditions. Starting version 10.1.3, this has been
eliminated and replaced by a proper cleanup of the result stack under error conditions.

Performance considerations with copying parameters

In theory, passing parameters to user-defined functions by value (memory copying) instead
by reference poses a potential disadvantage when dealing with large lists, arrays or strings.
But in practice newLISP performs faster or as fast than other scripting languages and offers
language facilities to pass very large memory object by reference.

Since newLISP version 9.4.5 functions can pass list, array and string type parameters
as references using default functor namespace ids. Namespaces (called contexts
in newLISP) have very little overhead and can be used to wrap functions and data.
This allows reference passing of large memory object into user-defined functions.

Since version 10.2 FOOP (Functional Object Oriented Programming) in newLISP also passes
the target object of a method call by reference.

But even in instances where reference passing and other optimizations are nor present,
the speed of ORO memory management more than compensates for the overhead required to
copy and delete objects.

Optimizations to ORO memory management ²

Since newLISP version 10.1, all lists, arrays and strings are passed in and out of
built-in functions by reference. All built-in functions work directly on memory
objects returned by reference from other built-in functions. This has substantially
reduced the need for copying and deleting memory objects and increased the speed of
some built-in functions. Now only parameters into user-defined functions and return
values passed out of user-defined functions are ORO managed.

Since version 10.3.2, newLISP checks the result stack before copying LISP cells.
This has reduced the amount of cells copied by about 83% and has significantly
increased the speed of many operations on bigger lists.

Memory and datatypes in newLISP

The memory objects of newLISP strings are allocated from and freed to the host's OS,
whenever newLISP recycles the cells from its allocated chunks of cell memory. This means
that newLISP handles cell memory more efficiently than string memory. As a result, it is
often better to use symbols rather than strings for efficient processing. For example,
when handling natural language it is more efficient to handle natural language words as
individual symbols in a separated name-space, then as single strings.
The bayes-train function in newLISP uses this method. newLISP can handle
millions of symbols without degrading performance.

Programmers coming from other programming languages frequently overlook that symbols in
LISP can act as more than just variables or object references. The symbol is a useful data
type in itself, which in many cases can replace the string data type.

Integer numbers and double floating-point numbers are stored directly in newLISP's LISP
cells and do not need a separate memory allocation cycle.

For efficiency during matrix operations like matrix multiplication or inversion, newLISP
allocates non-cell memory objects for matrices, converts the results to LISP cells, and then
frees the matrix memory objects.

newLISP allocates an array as a group of LISP cells. The LISP cells are allocated linearly.
As a result, array indices have faster random access to the LISP cells. Only a subset of
newLISP list functions can be used on arrays. Automatic memory management in newLISP handles
arrays in a manner similar to how it handles lists.

Implementing ORO memory management

The following pseudo code illustrates the algorithm implemented in newLISP in the context
of LISP expression evaluation. Only two functions and one data structure are necessary to
implement ORO memory management:

The first two functions pushResultStack and popResultStack push or
pop a LISP object handle on or off a stack. pushResultStack increases the value
resultStackIndex while popResultStack decreases it. In newLISP every
object is contained in a LISP cell structure. The object handle of that structure is simply
the memory pointer to the cell structure. The cell itself may contain pointer addresses to
other memory objects like string buffers or other LISP cells linked to the original object.
Small objects like numbers are stored directly. In this paper function
popResultStack() also implies that the popped object gets deleted.

The two resultStack management functions described are called by newLISP's
evaluateExpression function: ³

The function evaluateExpression introduces the two variables
resultStackIndexSave and resultStackIndex and a few other functions:

resultStackIndex is an index pointing to the top element in the
resultStack. The deeper the level of evaluation the higher the value of
resultStackIndex.

resultStackIndexSave serves as a temporary storage for the value of
resultStackIndex upon entry of the evaluateExpression(func, args) function.
Before exit the resultStack is popped to the saved level of resultStackIndex.
Popping the resultStack implies deleting the memory objects pointed to by entries in
the resultStack.

resultStack[] is a preallocated stack area for saving pointers to LISP cells
and indexed by resultStackIndex.

symbolContents(expr) and quoteContents(expr) extract contents from symbols
or quote-envelope cells.

typeOf(expr) extracts the type of an expression, which is either a BOOLEAN
constant like nil or true or a NUMBER or STRING, or is
a variable SYMBOL holding some contents, or a QUOTE serving as an envelope
to some other LIST expression expr.

evaluateFunc(func, args) is the application of a built-in function to its arguments.
The built-in function is the evaluated first member of a list in expr and the
arguments are the rest of the list in expr. The function func is
extracted calling evaluateExpression(first(expr)) recursively. For example if the
expression (expr is (foo x y) than foo is a built-in function and
x and y are the function arguments or parameters.

evaluateLambda(func, args) works similar to evaluateFunc(func, args), applying
a user-defined function first(expr) to its arguments in rest(expr). In case
of a user-defined function we have two types of arguments in rest(expr), a list of local
parameters followed by one or more body expressions evaluated in sequence.

Both, evaluateFunc(func, args) and evaluateLambda(func, args) will return
a newly created or copied LISP cell object, which may be any type of the above mentioned
expressions. Since version 10.0, many built-in functions processed with
evaluateFunc(func, args) are optimized and return references instead of a newly
created or copied objects. Except for these optimizations, result values will always
be newly created LISP cell objects destined to be destroyed on the next higher evaluation level,
after the current evaluateExpression(expr) function execution returned.

Both functions will recursively call evaluateExpression(expr) to evaluate their
arguments. As recursion deepens, the recursion level of the function increases.

Before evaluateExpression(func, args) returns, it will pop the resultStack
deleting the result values from deeper level of evaluation and returned by one of
the two functions, either evaluateFunc or evaluateLambda.

Any newly created result expression is destined to be destroyed later but its
deletion is delayed until a higher, less deep, level of evaluation is reached. This permits
results to be used and/or copied by calling functions.

The following example shows the evaluation of a small user-defined LISP function
sum-of-squares and the creation and deletion of associated memory objects:

The actual C-language implementation is optimized in some places to avoid pushing the
resultStack and avoid calling evaluateExpression(expr). Only the most
relevant steps are shown. The function evaluateLambda(func, args) does not need
to evaluate its arguments 3 and 4 because they are constants, but
evaluateLambda(func, args) will call evaluateExpression(expr) twice to
evaluate the two body expressions (+ (* x x) and (+ (* x x). Lines
preceded by the prompt > show the command-line entry.

evaluateLambda(func, args) also saves the environment for the variable symbols
x and y, copies parameters into local variables and restores the old
environment upon exit. These actions too involve creation and deletion of memory objects.
Details are omitted, because they are similar to methods in other dynamic languages.

¹ Reference counting and mark-and-sweep algorithms where specifically
developed for LISP. Other schemes like copying or generational algorithms where developed
for other languages like Smalltalk and later also used in LISP.